{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anomaly Detection in CANBus Traffic with LSTM and Autoencoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.externals import joblib\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from numpy.random import seed\n",
    "from tensorflow import set_random_seed\n",
    "import tensorflow as tf\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Input, Dropout, Dense, LSTM, TimeDistributed, RepeatVector\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras import regularizers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading and preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_FILE = \"data/can_generated/10_second_master_and_slave.csv\"\n",
    "preprocessed_data = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(CSV_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>event_type</th>\n",
       "      <th>priority</th>\n",
       "      <th>function</th>\n",
       "      <th>subfunction</th>\n",
       "      <th>node_id</th>\n",
       "      <th>len</th>\n",
       "      <th>d0</th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>d3</th>\n",
       "      <th>d4</th>\n",
       "      <th>d5</th>\n",
       "      <th>d6</th>\n",
       "      <th>d7</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1584317951333201</td>\n",
       "      <td>MSG_RX</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0xf0</td>\n",
       "      <td>0xf1</td>\n",
       "      <td>0x34</td>\n",
       "      <td>2</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1584317951333212</td>\n",
       "      <td>MSG_RX</td>\n",
       "      <td>0x01</td>\n",
       "      <td>0xf4</td>\n",
       "      <td>0xd0</td>\n",
       "      <td>0x10</td>\n",
       "      <td>6</td>\n",
       "      <td>0xFE</td>\n",
       "      <td>0xDE</td>\n",
       "      <td>0xBE</td>\n",
       "      <td>0xBE</td>\n",
       "      <td>0xCA</td>\n",
       "      <td>0xFE</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1584317951383198</td>\n",
       "      <td>MSG_RX</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0xf0</td>\n",
       "      <td>0xf1</td>\n",
       "      <td>0x34</td>\n",
       "      <td>2</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x01</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1584317951383210</td>\n",
       "      <td>MSG_RX</td>\n",
       "      <td>0x01</td>\n",
       "      <td>0xf4</td>\n",
       "      <td>0xd0</td>\n",
       "      <td>0x10</td>\n",
       "      <td>6</td>\n",
       "      <td>0xFE</td>\n",
       "      <td>0xDE</td>\n",
       "      <td>0xBE</td>\n",
       "      <td>0xBE</td>\n",
       "      <td>0xCA</td>\n",
       "      <td>0xFE</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1584317951433198</td>\n",
       "      <td>MSG_RX</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0xf0</td>\n",
       "      <td>0xf1</td>\n",
       "      <td>0x34</td>\n",
       "      <td>2</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x02</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>0x00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          timestamp event_type priority function subfunction node_id  len  \\\n",
       "0  1584317951333201     MSG_RX     0x00     0xf0        0xf1    0x34    2   \n",
       "1  1584317951333212     MSG_RX     0x01     0xf4        0xd0    0x10    6   \n",
       "2  1584317951383198     MSG_RX     0x00     0xf0        0xf1    0x34    2   \n",
       "3  1584317951383210     MSG_RX     0x01     0xf4        0xd0    0x10    6   \n",
       "4  1584317951433198     MSG_RX     0x00     0xf0        0xf1    0x34    2   \n",
       "\n",
       "     d0    d1    d2    d3    d4    d5    d6    d7  Unnamed: 15  \n",
       "0  0x00  0x00  0x00  0x00  0x00  0x00  0x00  0x00          NaN  \n",
       "1  0xFE  0xDE  0xBE  0xBE  0xCA  0xFE  0x00  0x00          NaN  \n",
       "2  0x00  0x01  0x00  0x00  0x00  0x00  0x00  0x00          NaN  \n",
       "3  0xFE  0xDE  0xBE  0xBE  0xCA  0xFE  0x00  0x00          NaN  \n",
       "4  0x00  0x02  0x00  0x00  0x00  0x00  0x00  0x00          NaN  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>priority</th>\n",
       "      <th>function</th>\n",
       "      <th>subfunction</th>\n",
       "      <th>node_id</th>\n",
       "      <th>len</th>\n",
       "      <th>d0</th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>d3</th>\n",
       "      <th>d4</th>\n",
       "      <th>d5</th>\n",
       "      <th>d6</th>\n",
       "      <th>d7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>0.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>1.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>402 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     priority  function  subfunction  node_id  len     d0     d1     d2  \\\n",
       "0         0.0     240.0        241.0     52.0  2.0    0.0    0.0    0.0   \n",
       "1         1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "2         0.0     240.0        241.0     52.0  2.0    0.0    1.0    0.0   \n",
       "3         1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "4         0.0     240.0        241.0     52.0  2.0    0.0    2.0    0.0   \n",
       "5         1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "6         0.0     240.0        241.0     52.0  2.0    0.0    3.0    0.0   \n",
       "7         1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "8         0.0     240.0        241.0     52.0  2.0    0.0    4.0    0.0   \n",
       "9         1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "10        0.0     240.0        241.0     52.0  2.0    0.0    5.0    0.0   \n",
       "11        1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "12        0.0     240.0        241.0     52.0  2.0    0.0    6.0    0.0   \n",
       "13        1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "14        0.0     240.0        241.0     52.0  2.0    0.0    7.0    0.0   \n",
       "15        1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "16        0.0     240.0        241.0     52.0  2.0    0.0    8.0    0.0   \n",
       "17        1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "18        0.0     240.0        241.0     52.0  2.0    0.0    9.0    0.0   \n",
       "19        1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "20        0.0     240.0        241.0     52.0  2.0    0.0   10.0    0.0   \n",
       "21        1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "22        0.0     240.0        241.0     52.0  2.0    0.0   11.0    0.0   \n",
       "23        1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "24        0.0     240.0        241.0     52.0  2.0    0.0   12.0    0.0   \n",
       "25        1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "26        0.0     240.0        241.0     52.0  2.0    0.0   13.0    0.0   \n",
       "27        1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "28        0.0     240.0        241.0     52.0  2.0    0.0   14.0    0.0   \n",
       "29        1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "..        ...       ...          ...      ...  ...    ...    ...    ...   \n",
       "372       0.0     240.0        241.0     52.0  2.0    0.0    6.0    0.0   \n",
       "373       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "374       0.0     240.0        241.0     52.0  2.0    0.0    7.0    0.0   \n",
       "375       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "376       0.0     240.0        241.0     52.0  2.0    0.0    8.0    0.0   \n",
       "377       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "378       0.0     240.0        241.0     52.0  2.0    0.0    9.0    0.0   \n",
       "379       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "380       0.0     240.0        241.0     52.0  2.0    0.0   10.0    0.0   \n",
       "381       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "382       0.0     240.0        241.0     52.0  2.0    0.0   11.0    0.0   \n",
       "383       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "384       0.0     240.0        241.0     52.0  2.0    0.0   12.0    0.0   \n",
       "385       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "386       0.0     240.0        241.0     52.0  2.0    0.0   13.0    0.0   \n",
       "387       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "388       0.0     240.0        241.0     52.0  2.0    0.0   14.0    0.0   \n",
       "389       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "390       0.0     240.0        241.0     52.0  2.0    0.0   15.0    0.0   \n",
       "391       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "392       0.0     240.0        241.0     52.0  2.0    0.0   16.0    0.0   \n",
       "393       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "394       0.0     240.0        241.0     52.0  2.0    0.0   17.0    0.0   \n",
       "395       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "396       0.0     240.0        241.0     52.0  2.0    0.0   18.0    0.0   \n",
       "397       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "398       0.0     240.0        241.0     52.0  2.0    0.0   19.0    0.0   \n",
       "399       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "400       0.0     240.0        241.0     52.0  2.0    0.0    0.0    0.0   \n",
       "401       1.0     244.0        208.0     16.0  6.0  254.0  222.0  190.0   \n",
       "\n",
       "        d3     d4     d5   d6   d7  \n",
       "0      0.0    0.0    0.0  0.0  0.0  \n",
       "1    190.0  202.0  254.0  0.0  0.0  \n",
       "2      0.0    0.0    0.0  0.0  0.0  \n",
       "3    190.0  202.0  254.0  0.0  0.0  \n",
       "4      0.0    0.0    0.0  0.0  0.0  \n",
       "5    190.0  202.0  254.0  0.0  0.0  \n",
       "6      0.0    0.0    0.0  0.0  0.0  \n",
       "7    190.0  202.0  254.0  0.0  0.0  \n",
       "8      0.0    0.0    0.0  0.0  0.0  \n",
       "9    190.0  202.0  254.0  0.0  0.0  \n",
       "10     0.0    0.0    0.0  0.0  0.0  \n",
       "11   190.0  202.0  254.0  0.0  0.0  \n",
       "12     0.0    0.0    0.0  0.0  0.0  \n",
       "13   190.0  202.0  254.0  0.0  0.0  \n",
       "14     0.0    0.0    0.0  0.0  0.0  \n",
       "15   190.0  202.0  254.0  0.0  0.0  \n",
       "16     0.0    0.0    0.0  0.0  0.0  \n",
       "17   190.0  202.0  254.0  0.0  0.0  \n",
       "18     0.0    0.0    0.0  0.0  0.0  \n",
       "19   190.0  202.0  254.0  0.0  0.0  \n",
       "20     0.0    0.0    0.0  0.0  0.0  \n",
       "21   190.0  202.0  254.0  0.0  0.0  \n",
       "22     0.0    0.0    0.0  0.0  0.0  \n",
       "23   190.0  202.0  254.0  0.0  0.0  \n",
       "24     0.0    0.0    0.0  0.0  0.0  \n",
       "25   190.0  202.0  254.0  0.0  0.0  \n",
       "26     0.0    0.0    0.0  0.0  0.0  \n",
       "27   190.0  202.0  254.0  0.0  0.0  \n",
       "28     0.0    0.0    0.0  0.0  0.0  \n",
       "29   190.0  202.0  254.0  0.0  0.0  \n",
       "..     ...    ...    ...  ...  ...  \n",
       "372    0.0    0.0    0.0  0.0  0.0  \n",
       "373  190.0  202.0  254.0  0.0  0.0  \n",
       "374    0.0    0.0    0.0  0.0  0.0  \n",
       "375  190.0  202.0  254.0  0.0  0.0  \n",
       "376    0.0    0.0    0.0  0.0  0.0  \n",
       "377  190.0  202.0  254.0  0.0  0.0  \n",
       "378    0.0    0.0    0.0  0.0  0.0  \n",
       "379  190.0  202.0  254.0  0.0  0.0  \n",
       "380    0.0    0.0    0.0  0.0  0.0  \n",
       "381  190.0  202.0  254.0  0.0  0.0  \n",
       "382    0.0    0.0    0.0  0.0  0.0  \n",
       "383  190.0  202.0  254.0  0.0  0.0  \n",
       "384    0.0    0.0    0.0  0.0  0.0  \n",
       "385  190.0  202.0  254.0  0.0  0.0  \n",
       "386    0.0    0.0    0.0  0.0  0.0  \n",
       "387  190.0  202.0  254.0  0.0  0.0  \n",
       "388    0.0    0.0    0.0  0.0  0.0  \n",
       "389  190.0  202.0  254.0  0.0  0.0  \n",
       "390    0.0    0.0    0.0  0.0  0.0  \n",
       "391  190.0  202.0  254.0  0.0  0.0  \n",
       "392    0.0    0.0    0.0  0.0  0.0  \n",
       "393  190.0  202.0  254.0  0.0  0.0  \n",
       "394    0.0    0.0    0.0  0.0  0.0  \n",
       "395  190.0  202.0  254.0  0.0  0.0  \n",
       "396    0.0    0.0    0.0  0.0  0.0  \n",
       "397  190.0  202.0  254.0  0.0  0.0  \n",
       "398    0.0    0.0    0.0  0.0  0.0  \n",
       "399  190.0  202.0  254.0  0.0  0.0  \n",
       "400    0.0    0.0    0.0  0.0  0.0  \n",
       "401  190.0  202.0  254.0  0.0  0.0  \n",
       "\n",
       "[402 rows x 13 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selección de columnas de interés\n",
    "preprocessed_data = df[\n",
    "    [\n",
    "        'priority','function','subfunction','node_id', # CANID\n",
    "        'len', # Length del payload\n",
    "        'd0','d1','d2','d3','d4','d5','d6','d7' # Payload\n",
    "    ]\n",
    "]\n",
    "preprocessed_data = preprocessed_data.apply(lambda x: x.astype(str).map(lambda x: float(int(x, base=16))))\n",
    "preprocessed_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling\n",
    "\n",
    "- Acá hay un púnto importante, no se debe usar MinMax porque los campos de los CANID son valores enumerados.\n",
    "- Debería pasar por otra etapa de transformación antes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.036036</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040541</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.045045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.049550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.063063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.036036</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040541</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.045045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.049550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.063063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.067568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.072072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.076577</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.081081</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.085586</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>402 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0    1    2    3    4    5         6    7    8    9    10   11   12\n",
       "0    0.0  0.0  1.0  1.0  0.0  0.0  0.000000  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "1    1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "2    0.0  0.0  1.0  1.0  0.0  0.0  0.004505  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "3    1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "4    0.0  0.0  1.0  1.0  0.0  0.0  0.009009  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "5    1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "6    0.0  0.0  1.0  1.0  0.0  0.0  0.013514  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "7    1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "8    0.0  0.0  1.0  1.0  0.0  0.0  0.018018  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "9    1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "10   0.0  0.0  1.0  1.0  0.0  0.0  0.022523  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "11   1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "12   0.0  0.0  1.0  1.0  0.0  0.0  0.027027  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "13   1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "14   0.0  0.0  1.0  1.0  0.0  0.0  0.031532  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "15   1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "16   0.0  0.0  1.0  1.0  0.0  0.0  0.036036  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "17   1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "18   0.0  0.0  1.0  1.0  0.0  0.0  0.040541  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "19   1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "20   0.0  0.0  1.0  1.0  0.0  0.0  0.045045  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "21   1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "22   0.0  0.0  1.0  1.0  0.0  0.0  0.049550  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "23   1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "24   0.0  0.0  1.0  1.0  0.0  0.0  0.054054  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "25   1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "26   0.0  0.0  1.0  1.0  0.0  0.0  0.058559  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "27   1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "28   0.0  0.0  1.0  1.0  0.0  0.0  0.063063  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "29   1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "..   ...  ...  ...  ...  ...  ...       ...  ...  ...  ...  ...  ...  ...\n",
       "372  0.0  0.0  1.0  1.0  0.0  0.0  0.027027  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "373  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "374  0.0  0.0  1.0  1.0  0.0  0.0  0.031532  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "375  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "376  0.0  0.0  1.0  1.0  0.0  0.0  0.036036  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "377  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "378  0.0  0.0  1.0  1.0  0.0  0.0  0.040541  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "379  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "380  0.0  0.0  1.0  1.0  0.0  0.0  0.045045  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "381  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "382  0.0  0.0  1.0  1.0  0.0  0.0  0.049550  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "383  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "384  0.0  0.0  1.0  1.0  0.0  0.0  0.054054  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "385  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "386  0.0  0.0  1.0  1.0  0.0  0.0  0.058559  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "387  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "388  0.0  0.0  1.0  1.0  0.0  0.0  0.063063  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "389  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "390  0.0  0.0  1.0  1.0  0.0  0.0  0.067568  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "391  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "392  0.0  0.0  1.0  1.0  0.0  0.0  0.072072  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "393  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "394  0.0  0.0  1.0  1.0  0.0  0.0  0.076577  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "395  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "396  0.0  0.0  1.0  1.0  0.0  0.0  0.081081  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "397  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "398  0.0  0.0  1.0  1.0  0.0  0.0  0.085586  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "399  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "400  0.0  0.0  1.0  1.0  0.0  0.0  0.000000  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "401  1.0  1.0  0.0  0.0  1.0  1.0  1.000000  1.0  1.0  1.0  1.0  0.0  0.0\n",
       "\n",
       "[402 rows x 13 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "x = preprocessed_data.values \n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "preprocessed_data = pd.DataFrame(x_scaled)\n",
    "preprocessed_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoencoder Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def autoencoder_model(X):\n",
    "    inputs = Input(shape=(X.shape[1], X.shape[2]))\n",
    "    L1 = LSTM(16, activation='relu', return_sequences=True, \n",
    "              kernel_regularizer=regularizers.l2(0.00))(inputs)\n",
    "    L2 = LSTM(4, activation='relu', return_sequences=False)(L1)\n",
    "    L3 = RepeatVector(X.shape[1])(L2)\n",
    "    L4 = LSTM(4, activation='relu', return_sequences=True)(L3) \n",
    "    L5 = LSTM(16, activation='relu', return_sequences=True)(L4)\n",
    "    output = TimeDistributed(Dense(X.shape[2]))(L5)    \n",
    "    model = Model(inputs=inputs, outputs=output)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is organized in a matrix of N_SAMPLES rows and 13 feature columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  (402, 13)\n",
      "Test:  (402, 13)\n"
     ]
    }
   ],
   "source": [
    "train = preprocessed_data\n",
    "print(\"Train: \", train.shape)\n",
    "test = preprocessed_data\n",
    "print(\"Test: \", test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Required format for LSTM input is (N_SAMPLES,N_TIMESTEPS,N_FEATURES)\n",
    "- TODO: HP tuning de N_TIMESTEPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  (402, 1, 13)\n",
      "Test:  (402, 1, 13)\n"
     ]
    }
   ],
   "source": [
    "X_train = train.values\n",
    "X_train = X_train.reshape(X_train.shape[0], 1, X_train.shape[1])\n",
    "print(\"Train: \", X_train.shape)\n",
    "\n",
    "X_test = test.values\n",
    "X_test = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])\n",
    "print(\"Test: \", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 1, 13)             0         \n",
      "_________________________________________________________________\n",
      "lstm_8 (LSTM)                (None, 1, 16)             1920      \n",
      "_________________________________________________________________\n",
      "lstm_9 (LSTM)                (None, 4)                 336       \n",
      "_________________________________________________________________\n",
      "repeat_vector_2 (RepeatVecto (None, 1, 4)              0         \n",
      "_________________________________________________________________\n",
      "lstm_10 (LSTM)               (None, 1, 4)              144       \n",
      "_________________________________________________________________\n",
      "lstm_11 (LSTM)               (None, 1, 16)             1344      \n",
      "_________________________________________________________________\n",
      "time_distributed_2 (TimeDist (None, 1, 13)             221       \n",
      "=================================================================\n",
      "Total params: 3,965\n",
      "Trainable params: 3,965\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = autoencoder_model(X_train)\n",
    "model.compile(optimizer='adam', loss='mae')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 381 samples, validate on 21 samples\n",
      "Epoch 1/100\n",
      "381/381 [==============================] - 4s 11ms/step - loss: 0.4229 - val_loss: 0.4351\n",
      "Epoch 2/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.4212 - val_loss: 0.4335\n",
      "Epoch 3/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.4202 - val_loss: 0.4313\n",
      "Epoch 4/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.4017 - val_loss: 0.3460\n",
      "Epoch 5/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.1846 - val_loss: 0.1170\n",
      "Epoch 6/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0971 - val_loss: 0.0777\n",
      "Epoch 7/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0737 - val_loss: 0.0635\n",
      "Epoch 8/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0652 - val_loss: 0.0637\n",
      "Epoch 9/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0610 - val_loss: 0.0571\n",
      "Epoch 10/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0564 - val_loss: 0.0512\n",
      "Epoch 11/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0491 - val_loss: 0.0427\n",
      "Epoch 12/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0353 - val_loss: 0.0232\n",
      "Epoch 13/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0144 - val_loss: 0.0080\n",
      "Epoch 14/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0047 - val_loss: 0.0054\n",
      "Epoch 15/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0031\n",
      "Epoch 16/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0047\n",
      "Epoch 17/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0026\n",
      "Epoch 18/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0025 - val_loss: 0.0072\n",
      "Epoch 19/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.0034\n",
      "Epoch 20/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0031\n",
      "Epoch 21/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0022 - val_loss: 0.0037\n",
      "Epoch 22/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0025 - val_loss: 0.0018\n",
      "Epoch 23/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0018 - val_loss: 0.0028\n",
      "Epoch 24/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0020 - val_loss: 0.0021\n",
      "Epoch 25/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0020 - val_loss: 0.0028\n",
      "Epoch 26/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0024 - val_loss: 0.0045\n",
      "Epoch 27/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0034\n",
      "Epoch 28/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0030 - val_loss: 0.0021\n",
      "Epoch 29/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0020 - val_loss: 0.0021\n",
      "Epoch 30/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0018 - val_loss: 0.0027\n",
      "Epoch 31/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0022 - val_loss: 0.0054\n",
      "Epoch 32/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0039 - val_loss: 0.0053\n",
      "Epoch 33/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.0026\n",
      "Epoch 34/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0024 - val_loss: 0.0020\n",
      "Epoch 35/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0023 - val_loss: 0.0041\n",
      "Epoch 36/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0027\n",
      "Epoch 37/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025\n",
      "Epoch 38/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0021 - val_loss: 0.0020\n",
      "Epoch 39/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0021 - val_loss: 0.0028\n",
      "Epoch 40/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0023 - val_loss: 0.0029\n",
      "Epoch 41/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0022 - val_loss: 0.0023\n",
      "Epoch 42/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0033\n",
      "Epoch 43/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0023 - val_loss: 0.0024\n",
      "Epoch 44/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0022 - val_loss: 0.0038\n",
      "Epoch 45/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0024 - val_loss: 0.0042\n",
      "Epoch 46/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0021\n",
      "Epoch 47/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0020 - val_loss: 0.0018\n",
      "Epoch 48/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0019 - val_loss: 0.0025\n",
      "Epoch 49/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0022 - val_loss: 0.0024\n",
      "Epoch 50/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0019 - val_loss: 0.0031\n",
      "Epoch 51/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0041\n",
      "Epoch 52/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0024 - val_loss: 0.0020\n",
      "Epoch 53/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0038\n",
      "Epoch 54/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0043 - val_loss: 0.0033\n",
      "Epoch 55/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0040 - val_loss: 0.0032\n",
      "Epoch 56/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0029 - val_loss: 0.0041\n",
      "Epoch 57/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0024\n",
      "Epoch 58/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0023\n",
      "Epoch 59/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0020 - val_loss: 0.0021\n",
      "Epoch 60/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0021 - val_loss: 0.0023\n",
      "Epoch 61/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0026\n",
      "Epoch 62/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0022 - val_loss: 0.0020\n",
      "Epoch 63/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0020 - val_loss: 0.0024\n",
      "Epoch 64/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0040\n",
      "Epoch 65/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0040\n",
      "Epoch 66/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0022\n",
      "Epoch 67/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0023 - val_loss: 0.0029\n",
      "Epoch 68/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0019 - val_loss: 0.0027\n",
      "Epoch 69/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0024 - val_loss: 0.0027\n",
      "Epoch 70/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0021 - val_loss: 0.0023\n",
      "Epoch 71/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0019 - val_loss: 0.0025\n",
      "Epoch 72/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0021 - val_loss: 0.0034\n",
      "Epoch 73/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0029 - val_loss: 0.0032\n",
      "Epoch 74/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0031 - val_loss: 0.0022\n",
      "Epoch 75/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0034 - val_loss: 0.0022\n",
      "Epoch 76/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0027 - val_loss: 0.0026\n",
      "Epoch 77/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0020 - val_loss: 0.0024\n",
      "Epoch 78/100\n",
      "381/381 [==============================] - 1s 2ms/step - loss: 0.0019 - val_loss: 0.0021\n",
      "Epoch 79/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0023 - val_loss: 0.0041\n",
      "Epoch 80/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0027 - val_loss: 0.0041\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 81/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.0023\n",
      "Epoch 82/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0033\n",
      "Epoch 83/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0025 - val_loss: 0.0019\n",
      "Epoch 84/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0017 - val_loss: 0.0022\n",
      "Epoch 85/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0020 - val_loss: 0.0032\n",
      "Epoch 86/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0024 - val_loss: 0.0020\n",
      "Epoch 87/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0020 - val_loss: 0.0024\n",
      "Epoch 88/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0052\n",
      "Epoch 89/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0029 - val_loss: 0.0021\n",
      "Epoch 90/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0020 - val_loss: 0.0022\n",
      "Epoch 91/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0018 - val_loss: 0.0039\n",
      "Epoch 92/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0038 - val_loss: 0.0026\n",
      "Epoch 93/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0019 - val_loss: 0.0033\n",
      "Epoch 94/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0022\n",
      "Epoch 95/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0022 - val_loss: 0.0030\n",
      "Epoch 96/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0019 - val_loss: 0.0043\n",
      "Epoch 97/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0028 - val_loss: 0.0025\n",
      "Epoch 98/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0029 - val_loss: 0.0037\n",
      "Epoch 99/100\n",
      "381/381 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0017\n",
      "Epoch 100/100\n",
      "381/381 [==============================] - 1s 1ms/step - loss: 0.0019 - val_loss: 0.0022\n"
     ]
    }
   ],
   "source": [
    "nb_epochs = 100\n",
    "batch_size = 10\n",
    "history = model.fit( X_train, X_train, epochs=nb_epochs, batch_size=batch_size,\n",
    "                    validation_split=0.05).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(402, 13)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pred = model.predict(X_train)\n",
    "X_pred = X_pred.reshape(X_pred.shape[0], X_pred.shape[2])\n",
    "X_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.0166820e-03,  2.5472976e-04,  1.0025184e+00,  1.0019031e+00,\n",
       "       -9.5160212e-05,  2.9165298e-04,  4.0570963e-02,  4.4429209e-04,\n",
       "        5.0836336e-04,  1.6875938e-04,  8.0302358e-05, -1.4006440e-04,\n",
       "       -3.2568513e-04], dtype=float32)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pred[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(402, 13)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pred = pd.DataFrame(X_pred, columns=preprocessed_data.columns)\n",
    "X_pred.shape\n",
    "#X_pred.index = preprocessed_data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(402, 13)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain = X_train.reshape(X_train.shape[0], X_train.shape[2])\n",
    "Xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1280x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the loss distribution of the training set\n",
    "X_pred = model.predict(X_train)\n",
    "X_pred = X_pred.reshape(X_pred.shape[0], X_pred.shape[2])\n",
    "X_pred = pd.DataFrame(X_pred, columns=train.columns)\n",
    "X_pred.index = train.index\n",
    "\n",
    "scored = pd.DataFrame(index=train.index)\n",
    "Xtrain = X_train.reshape(X_train.shape[0], X_train.shape[2])\n",
    "scored['Loss_mae'] = np.mean(np.abs(X_pred-Xtrain), axis = 1)\n",
    "plt.figure(figsize=(16,9), dpi=80)\n",
    "plt.title('Loss Distribution', fontsize=16)\n",
    "sns.distplot(scored['Loss_mae'], bins = 20, kde= True, color = 'blue');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loss_mae</th>\n",
       "      <th>Threshold</th>\n",
       "      <th>Anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.003717</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001736</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003371</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001736</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003025</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Loss_mae  Threshold  Anomaly\n",
       "0  0.003717     0.0045    False\n",
       "1  0.001736     0.0045    False\n",
       "2  0.003371     0.0045    False\n",
       "3  0.001736     0.0045    False\n",
       "4  0.003025     0.0045    False"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pred = model.predict(X_test)\n",
    "X_pred = X_pred.reshape(X_pred.shape[0], X_pred.shape[2])\n",
    "X_pred = pd.DataFrame(X_pred, columns=test.columns)\n",
    "X_pred.index = test.index\n",
    "\n",
    "scored = pd.DataFrame(index=test.index)\n",
    "Xtest = X_test.reshape(X_test.shape[0], X_test.shape[2])\n",
    "scored['Loss_mae'] = np.mean(np.abs(X_pred-Xtest), axis = 1)\n",
    "scored['Threshold'] = 0.0045\n",
    "scored['Anomaly'] = scored['Loss_mae'] > scored['Threshold']\n",
    "scored.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc77ffa4f60>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scored.plot(logy=True,  figsize=(16,9), color=['blue','red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "model.save(\"master_and_slave.h5\")\n",
    "print(\"Model saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_file(csv_file):\n",
    "    test = pd.read_csv(csv_file)\n",
    "\n",
    "    # Selección de columnas de interés\n",
    "    test = test[\n",
    "        [\n",
    "            'priority','function','subfunction','node_id', # CANID\n",
    "            'len', # Length del payload\n",
    "            'd0','d1','d2','d3','d4','d5','d6','d7' # Payload\n",
    "        ]\n",
    "    ]\n",
    "    test = test.apply(lambda x: x.astype(str).map(lambda x: float(int(x, base=16))))\n",
    "    x = test.values \n",
    "    min_max_scaler = preprocessing.MinMaxScaler()\n",
    "    x_scaled = min_max_scaler.fit_transform(x)\n",
    "    test = pd.DataFrame(x_scaled)\n",
    "\n",
    "    X_test = test.values\n",
    "    X_test = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])\n",
    "\n",
    "    # calculate the loss on the test set\n",
    "    X_pred = model.predict(X_test)\n",
    "    X_pred = X_pred.reshape(X_pred.shape[0], X_pred.shape[2])\n",
    "    X_pred = pd.DataFrame(X_pred, columns=test.columns)\n",
    "    X_pred.index = test.index\n",
    "\n",
    "    scored = pd.DataFrame(index=test.index)\n",
    "    Xtest = X_test.reshape(X_test.shape[0], X_test.shape[2])\n",
    "    scored['Loss_mae'] = np.mean(np.abs(X_pred-Xtest), axis = 1)\n",
    "    scored['Threshold'] = 0.0045\n",
    "    scored['Anomaly'] = scored['Loss_mae'] > scored['Threshold']\n",
    "    #scored.head()\n",
    "\n",
    "    # plot bearing failure time plot\n",
    "    scored.plot(logy=True,  figsize=(16,9), color=['blue','red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "process_file(\"data/can_generated/10_second_master_and_slave_broken.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
